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Sponsor Presentation—Advancements in High-Performance AI/ML through PyTorch's Python Compiler
Discover PyTorch's Python compiler, Dynamo, designed to prioritize user experience for AI researchers, with dynamic control flow and ability to handle input changes, showcasing 93% successful model deployment.
- PyTorch’s popularity is largely due to its Pythonic approach, which makes it easy to use.
- The project’s success can be attributed to its early adoption of a comprehensive set of Python features, collected by the PyTorch team.
- The compiler, Dynamo, is designed to prioritize user experience, particularly for AI researchers, and is not a static data flow graph.
- The compiler executes a program line by line, recording or tracing each PyTorch operation.
- The compiler emits code for run on GPU, and generates code for dynamic control flow, allowing it to handle operations like input changes.
- TorchScript is an earlier attempt at adding a compiler to PyTorch, which worked by tracing the execution of a program and generating a graph.
- The Torch.jit.trace compiler works by adding guards to handle operations that weren’t captured by the PyTorch model.
- The new compiler, Dynamo, takes a different approach by splitting the effort into three steps: graph acquisition, graph lowering, and graph compilation.
- The compiler can handle dynamic control flow and is not limited to static data flow graphs.
- The results show that 93% of the 80 models worked out of the box with the new compiler.
- The compiler is designed to work with upcoming experimental hardware and is still under development.
- The project is open-source and welcomes contributions.
- The developer community is active and provides support through the discourse forum.